I have a large set of simulation data that was generated using a Monte Carlo procedure that samples a distribution parameterized by (h, t). I want to use an InfoGAN model to provide unsupervised predictions classifications c of the simulation samples.

Now, it would be easy enough to just use the standard InfoGAN structure to do the unsupervised classification without using the external parameters (h, t), but I want the neural network to learn how c depends on (h, t). I would think that simply providing (h, t) as additional inputs to the generator and the discriminator will not provide the best results considering that for some parameters h_i and t_j that c_k is not guaranteed to exist. So, I cannot just independently sample h, t, and c when providing input for the generator. Instead, I would like InfoGAN to learn and optimize the I(c(h, t); G(z, c(h, t))) instead of I(c; G(z, c)) where c(h, t) is not known a priori.

In short, I suppose I am looking for a way to combine the unsupervised classification of InfoGAN while also providing additional conditional information as with CGAN in a way such that the relationship between the classification and the conditional information is learned.

From the project on GitHub:

A hierarchical self-organizing map (HSOM) is an unsupervised neural network that learns patterns from high-dimensional space and represents them in lower dimensions.

HSOM networks recieve inputs and feed them into a set of self-organizing maps, each learning individual features of the input space. These maps produce sparse output vectors with only the most responsive nodes activating, a result of competitive inhibition which restricts the number of ‘winners’ (i.e. active nodes) allowed at any given time.

Each layer in an HSOM network contains a set of maps that view part of the input space and generate sparse output vectors, which together form the input for the next layer in the hierarchy. Information becomes increasingly abstract as it is passed through the network and ultimately results in a low-dimensional sparse representation of the original data.

The training process results in a model that maps certain input patterns to certain labels, corresponding to high-dimensional and low-dimensional data respectively. Given that training is unsupervised, the labels have no intrinsic meaning but rather become meaningful through their repeated association with certain input patterns and their relative lack of association with others. Put simply, labels come to represent higher-dimensional patterns over time, allowing them to be distinguished from one another in a meaningful way.

ICLR 2020 paper acceptance results are supposed to be released on 19th December 2019. Creating a discussion thread for this year’s results.

I have very limited experience with ML, so apologies if this question is silly or too abstract.

Many problems (e.g. abstractive text summarization) do not have well-developed and robust solutions yet, even though the progress is reasonably fast and there is a lot of theoretical research that allows us to move forward.

For example, developments in the area of word embeddings have made a huge impact on the quality of text processing models. We have come a long way from the simplest Bag-of-Words model to more modern Word2Vec variants to Glove and FastText. Thanks to these developments, we are able to train models which successfully catch on the semantics of text. However, this required decades of research, which is a long time.

This kind of research yields new ideas and concepts, not just results of computations or definite answers to specific questions. This applies to any area (biology, chemistry, physics), not just text processing.

So, my basic question is, could we make a computer research this kind of problem instead of spending the time of actual humans? I’m not even sure if this lies in the realm of ML, but it doesn’t seem as hard as creating a “true AI”, because such “machine thinker” would only need knowledge of some subject area, not a complete memory of an adult human.

Basically, can we create ML models which output ideas and concepts as opposed to specific answers to classification or prediction problems? E.g. can we have a computer “invent” the next approach to word embeddings (better than current state-of-the-art) faster than the human researchers will?

It’s not even necessary that the resulting approach is understood by humans, it just needs to be implementable.

I see a lot of unsolved problems here (how do we formalize ideas to make the machine process them? where do we get training datasets with “good” and “bad” ideas?), but is there any research at all into this sort of thing?

Thanks!

P.S. Let’s keep jokes about AI apocalypse out of this

Hey guys, I’m Alim. I work as a technical support associate in IBM and I have a BE in mechanical. Im looking towards shifting to machine learning as this is what interests me a lot and I’ve really been wanting to work in this field.

I wanna know what would be the best approach I could take in order to slowly learn and get better at it and eventually be able to work with maximum efficiency. I have a lot of time on my hands as in I have got around a year before I can apply for internal job postings within this field. So it would be great if anybody could suggest me a course and things to do which would help me.

Thanks in advance. Have a wonderful day! 🙂

Hi all !

My company has a Keras project where the deployment is currently handled by a homemade Flask API and custom bash scripts. We’re having trouble

• versioning our code, models and data,
• going from experiments to production-ready code,
• designing clear standards and automatic release checks.

We’re looking for one (or several combined) ML framework that would help us solve these issues. So far we’re benchmarking TFX, MLFlow, Floydhub, Acumos, Neptune, DVC and Pachyderm, but we wonder if we didn’t miss a good candidate.

Did a framework change your life ? Share your experience !

Does it really matter what I major in (CS or Stat) for my undergrad degree, especially if I plan on doing a MLE or Data Science program in grad school? Would it be more beneficial to have a particular major/minor combination (i.e. major in CS and minor in math/stat) now?